From ba5c7d1630d6daf15340645bed6ab4b7828f2500 Mon Sep 17 00:00:00 2001 From: CI-DEV <154627941+IlumCI@users.noreply.github.com> Date: Fri, 26 Sep 2025 22:14:26 +0300 Subject: [PATCH] Create election_swarm.md --- docs/swarms/structs/election_swarm.md | 880 ++++++++++++++++++++++++++ 1 file changed, 880 insertions(+) create mode 100644 docs/swarms/structs/election_swarm.md diff --git a/docs/swarms/structs/election_swarm.md b/docs/swarms/structs/election_swarm.md new file mode 100644 index 00000000..f815213d --- /dev/null +++ b/docs/swarms/structs/election_swarm.md @@ -0,0 +1,880 @@ +# ElectionSwarm + +The `ElectionSwarm` is a sophisticated multi-agent orchestrator selection system that implements AGENTSNET communication protocols for democratic decision-making. It enables agent voters to elect the best orchestrator candidate through various coordination algorithms, providing a structured approach to leadership selection in multi-agent workflows. + +## Overview + +The ElectionSwarm follows a democratic election workflow pattern: + +1. **Task Reception**: User provides an election task to the swarm +2. **Voter Registration**: Agent voters are registered with their preferences and expertise +3. **Candidate Presentation**: Orchestrator candidates present their platforms and capabilities +4. **Message Passing**: Voters communicate with neighbors using AGENTSNET protocols +5. **Voting Process**: Voters cast structured votes with detailed reasoning +6. **Result Determination**: Election algorithms determine the winning orchestrator +7. **Context Preservation**: All conversation history and voting records are maintained + +## Architecture + +```mermaid +graph TD + A[User Task] --> B[ElectionSwarm] + B --> C[Voter Registration] + B --> D[Candidate Registration] + C --> E[Agent Voters] + D --> F[Orchestrator Candidates] + E --> G[Message Passing Protocol] + F --> G + G --> H[AGENTSNET Communication] + H --> I[Voting Process] + I --> J[Election Algorithms] + J --> K[Consensus Building] + J --> L[Leader Election] + J --> M[Matching Algorithm] + J --> N[Coloring Algorithm] + J --> O[Vertex Cover] + K --> P[Election Result] + L --> P + M --> P + N --> P + O --> P + P --> Q[Selected Orchestrator] +``` + +## Key Features + +| Feature | Description | +|---------|-------------| +| **AGENTSNET Protocols** | Implements distributed computing algorithms for coordination | +| **Message Passing** | Synchronous neighbor-to-neighbor communication in rounds | +| **Structured Voting** | Voters provide detailed reasoning, confidence, and candidate rankings | +| **Multiple Algorithms** | Consensus, Leader Election, Matching, Coloring, Vertex Cover | +| **Cost Tracking** | Budget management and token usage monitoring | +| **Caching System** | Response caching to avoid redundant LLM calls | +| **Lazy Loading** | Agents instantiated only when needed | +| **Parallel Execution** | Concurrent agent operations for efficiency | +| **Comprehensive Logging** | Detailed logging for debugging and monitoring | +| **Flexible Configuration** | Configurable election parameters and algorithms | +| **Voter Tool Calls** | Optional tool calls for detailed voting explanations | + +## ElectionSwarm Constructor + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `name` | `str` | `"ElectionSwarm"` | Name of the election swarm | +| `description` | `str` | `"Orchestrator selection with AGENTSNET algorithms"` | Description of the swarm purpose | +| `voters` | `List[VoterProfile]` | `None` | List of agent voters (defaults created if None) | +| `candidates` | `List[CandidateProfile]` | `None` | List of orchestrator candidates (defaults created if None) | +| `election_config` | `ElectionConfig` | `None` | Election configuration settings | +| `max_loops` | `int` | `1` | Maximum number of election loops | +| `output_type` | `OutputType` | `"dict-all-except-first"` | Output format type | +| `verbose` | `bool` | `False` | Enable verbose logging | +| `enable_lazy_loading` | `bool` | `True` | Enable lazy agent loading | +| `enable_caching` | `bool` | `True` | Enable response caching | +| `batch_size` | `int` | `25` | Batch size for parallel operations | +| `budget_limit` | `float` | `200.0` | Budget limit for cost tracking | +| `*args` | `Any` | - | Additional positional arguments | +| `**kwargs` | `Any` | - | Additional keyword arguments | + +## Core Components + +### VoterProfile + +Represents an agent voter in the orchestrator selection system. + +```python +@dataclass +class VoterProfile: + voter_id: str + name: str + voter_type: VoterType + preferences: Dict[str, Any] = field(default_factory=dict) + expertise_areas: List[str] = field(default_factory=list) + voting_weight: float = 1.0 + agent: Optional[Agent] = None + is_loaded: bool = False + demographics: Dict[str, Any] = field(default_factory=dict) + past_voting_history: List[Dict[str, Any]] = field(default_factory=list) + neighbors: List[str] = field(default_factory=list) + coordination_style: str = "collaborative" + leadership_preferences: Dict[str, float] = field(default_factory=dict) +``` + +### VoterType + +Enumeration of voter types in the election system. + +```python +class VoterType(str, Enum): + INDIVIDUAL = "individual" + GROUP = "group" + EXPERT = "expert" + DELEGATE = "delegate" +``` + +### CandidateProfile + +Represents an orchestrator candidate in the selection system. + +```python +@dataclass +class CandidateProfile: + candidate_id: str + name: str + candidate_type: CandidateType + party_affiliation: Optional[str] = None + policy_positions: Dict[str, Any] = field(default_factory=dict) + campaign_promises: List[str] = field(default_factory=list) + experience: List[str] = field(default_factory=list) + agent: Optional[Agent] = None + is_loaded: bool = False + support_base: Dict[str, float] = field(default_factory=dict) + campaign_strategy: Dict[str, Any] = field(default_factory=dict) + leadership_style: str = "collaborative" + coordination_approach: Dict[str, Any] = field(default_factory=dict) + technical_expertise: List[str] = field(default_factory=list) +``` + +### CandidateType + +Enumeration of candidate types in the election system. + +```python +class CandidateType(str, Enum): + INDIVIDUAL = "individual" + COALITION = "coalition" + PARTY = "party" + MOVEMENT = "movement" +``` + +### ElectionAlgorithm + +AGENTSNET algorithms for coordination. + +```python +class ElectionAlgorithm(str, Enum): + CONSENSUS = "consensus" + LEADER_ELECTION = "leader_election" + MATCHING = "matching" + COLORING = "coloring" + VERTEX_COVER = "vertex_cover" +``` + +### VoterDecision + +Structured output from voter agents. + +```python +@dataclass +class VoterDecision: + voter_id: str + rationality: str + vote: VoteResult + confidence: float = 0.0 + reasoning_factors: List[str] = field(default_factory=list) + candidate_rankings: Dict[str, int] = field(default_factory=dict) + tool_call_explanation: Optional[str] = None + timestamp: datetime = field(default_factory=datetime.now) +``` + +### VoteResult + +Possible vote results. + +```python +class VoteResult(str, Enum): + FOR = "for" + AGAINST = "against" + ABSTAIN = "abstain" + INVALID = "invalid" +``` + +### ElectionResult + +Results of an election. + +```python +@dataclass +class ElectionResult: + election_id: str + algorithm_used: ElectionAlgorithm + total_voters: int + total_candidates: int + votes_cast: int + winner: Optional[str] = None + vote_distribution: Dict[str, int] = field(default_factory=dict) + voter_decisions: List[VoterDecision] = field(default_factory=list) + consensus_reached: bool = False + rounds_to_consensus: int = 0 + timestamp: datetime = field(default_factory=datetime.now) +``` + +### MessagePassingProtocol + +AGENTSNET-inspired message-passing protocol. + +```python +class MessagePassingProtocol: + def __init__(self, rounds: int = 5, synchronous: bool = True): + self.rounds = rounds + self.synchronous = synchronous + self.current_round = 0 + self.message_history: List[Dict[str, Any]] = [] +``` + +### CostTracker + +Track costs and usage for budget management. + +```python +@dataclass +class CostTracker: + total_tokens_used: int = 0 + total_cost_estimate: float = 0.0 + budget_limit: float = DEFAULT_BUDGET_LIMIT + token_cost_per_1m: float = 0.15 + requests_made: int = 0 + cache_hits: int = 0 +``` + +## Voter Tool Calls + +The ElectionSwarm supports optional tool calls that allow voter agents to provide detailed explanations of their voting decisions. When enabled, each voter agent will make a tool call to `explain_voting_decision` before casting their vote. + +### Tool Call Features + +- **Detailed Explanations**: Voters provide comprehensive reasoning for their choices +- **Structured Data**: Tool calls include voter name, ID, chosen candidate, and reasoning +- **Confidence Levels**: Voters specify their confidence in their decision +- **Alternative Considerations**: Voters explain why other candidates were not chosen +- **Key Factors**: Voters list the most important factors influencing their decision + +### Tool Call Structure + +```python +{ + "voter_name": "Alice Johnson", + "voter_id": "voter_001", + "chosen_candidate": "John Progressive", + "voting_reasoning": "Detailed explanation of why this candidate was chosen", + "key_factors": ["healthcare policy", "environmental stance", "experience"], + "confidence_level": 0.85, + "alternative_considerations": "Considered Sarah Conservative but disagreed on healthcare" +} +``` + +### Enabling Tool Calls + +```python +# Enable voter tool calls in configuration +config = ElectionConfig( + config_data={ + "enable_voter_tool_calls": True, + # ... other config options + } +) + +election = ElectionSwarm( + election_config=config, + # ... other parameters +) +``` + +## AGENTSNET Communication Protocols + +### Message Passing Protocol + +The ElectionSwarm implements AGENTSNET-inspired message-passing for distributed coordination: + +- **Synchronous Communication**: Agents communicate in discrete rounds +- **Neighbor-Only Messaging**: Agents can only communicate with immediate neighbors +- **JSON Message Format**: Structured message exchange +- **Multi-Round Process**: Multiple rounds for information propagation +- **Final Decision**: Agents make final decisions after communication rounds + +### Communication Flow + +```mermaid +sequenceDiagram + participant V1 as Voter 1 + participant V2 as Voter 2 + participant V3 as Voter 3 + participant ES as ElectionSwarm + + Note over ES: Round 1: Initial Communication + V1->>V2: Share candidate preferences + V2->>V1: Share candidate preferences + V2->>V3: Share candidate preferences + V3->>V2: Share candidate preferences + + Note over ES: Round 2: Information Exchange + V1->>V2: Discuss reasoning factors + V2->>V1: Discuss reasoning factors + V2->>V3: Discuss reasoning factors + V3->>V2: Discuss reasoning factors + + Note over ES: Round 3: Final Consensus + V1->>ES: Final vote with reasoning + V2->>ES: Final vote with reasoning + V3->>ES: Final vote with reasoning +``` + +## Election Algorithms + +The ElectionSwarm implements five AGENTSNET-inspired algorithms for orchestrator selection. Each algorithm is designed for different coordination scenarios and communication patterns. + +### 1. Consensus Algorithm + +All agents must agree on a single orchestrator candidate through multiple rounds of communication. + +- **Purpose**: Achieve unanimous agreement on orchestrator selection +- **Process**: Multiple rounds until consensus threshold is reached +- **Threshold**: Configurable consensus threshold (default: 60%) +- **Fallback**: Majority vote if consensus not reached +- **Implementation**: `_conduct_consensus_election()` method +- **Use Case**: When unanimous agreement is required + +### 2. Leader Election Algorithm + +Select a single leader orchestrator through distributed voting. + +- **Purpose**: Elect one orchestrator to lead the multi-agent workflow +- **Process**: Direct voting with winner-takes-all mechanism +- **Complexity**: O(D) rounds where D is network diameter +- **Implementation**: `_conduct_leader_election()` method +- **Use Case**: When clear leadership hierarchy is needed + +### 3. Matching Algorithm + +Pair voters with compatible orchestrator candidates using maximal matching. + +- **Purpose**: Create optimal voter-candidate pairings +- **Process**: Maximal matching to avoid conflicts +- **Complexity**: O(log* n) rounds +- **Implementation**: `_conduct_matching_election()` method +- **Use Case**: When specialized orchestrator-voter relationships are important + +### 4. Coloring Algorithm + +Group voters and candidates into compatible categories using graph coloring. + +- **Purpose**: Assign roles such that neighbors have different roles +- **Process**: (Δ+1)-coloring where Δ is maximum node degree +- **Complexity**: O(log* n) rounds +- **Implementation**: `_conduct_coloring_election()` method +- **Use Case**: Role assignment with conflict avoidance + +### 5. Vertex Cover Algorithm + +Select minimal set of coordinator agents to cover all voters. + +- **Purpose**: Choose minimal set of orchestrators to cover all voters +- **Process**: Minimal vertex cover selection +- **Complexity**: O(log* n) rounds +- **Implementation**: `_conduct_vertex_cover_election()` method +- **Use Case**: When resource-efficient coordination is needed + +### Algorithm Selection + +Choose the appropriate algorithm based on your coordination needs: + +```python +# For unanimous decisions +ElectionAlgorithm.CONSENSUS + +# For clear leadership +ElectionAlgorithm.LEADER_ELECTION + +# For optimal pairings +ElectionAlgorithm.MATCHING + +# For role assignment +ElectionAlgorithm.COLORING + +# For minimal coordination +ElectionAlgorithm.VERTEX_COVER +``` + +## Usage Examples + +### Basic Election Setup + +```python +from swarms.structs.election_swarm import ( + ElectionSwarm, + VoterProfile, + CandidateProfile, + VoterType, + CandidateType, + ElectionAlgorithm +) + +# Create agent voters +voters = [ + VoterProfile( + voter_id="agent_001", + name="Data Analysis Agent", + voter_type=VoterType.EXPERT, + preferences={ + "technical_leadership": 0.9, + "data_processing": 0.8, + "workflow_efficiency": 0.6 + }, + expertise_areas=["data_science", "machine_learning"], + neighbors=["Research Agent", "Backend Agent"] + ), + # ... more voters +] + +# Create orchestrator candidates +candidates = [ + CandidateProfile( + candidate_id="orchestrator_001", + name="Technical Lead Orchestrator", + candidate_type=CandidateType.INDIVIDUAL, + policy_positions={ + "technical_leadership": "Focus on code quality and architecture", + "team_coordination": "Agile methodologies with clear planning" + }, + campaign_promises=[ + "Implement comprehensive code review processes", + "Establish automated testing pipelines" + ], + leadership_style="transformational", + technical_expertise=["software_architecture", "devops"] + ), + # ... more candidates +] + +# Create election swarm +election = ElectionSwarm( + name="Multi-Agent Orchestrator Election", + voters=voters, + candidates=candidates, + verbose=True +) +``` + +### Running Elections + +```python +# Run consensus election +result = election.run( + task="Select the best orchestrator for our multi-agent workflow", + election_type=ElectionAlgorithm.CONSENSUS, + max_rounds=5 +) + +# Run leader election +result = election.run( + task="Elect a leader to coordinate our development team", + election_type=ElectionAlgorithm.LEADER_ELECTION +) + +# Run matching election +result = election.run( + task="Match voters with compatible orchestrator candidates", + election_type=ElectionAlgorithm.MATCHING +) + +# Run election session with comprehensive analysis +session_result = election.run_election_session( + election_type=ElectionAlgorithm.CONSENSUS, + max_rounds=5 +) + +# Access detailed results +election_result = session_result["election_result"] +analysis = session_result["analysis"] +cost_stats = session_result["cost_statistics"] +``` + +### Board CEO Election Example + +```python +# Create board of directors as voters +board_members = [ + VoterProfile( + voter_id="director_001", + name="Sarah Chen - Technology Director", + voter_type=VoterType.EXPERT, + preferences={ + "innovation": 0.9, + "technical_excellence": 0.8, + "digital_transformation": 0.7 + }, + expertise_areas=["technology", "innovation"], + coordination_style="data_driven", + leadership_preferences={ + "technical_leadership": 0.9, + "innovation": 0.8 + } + ), + # ... more board members +] + +# Create CEO candidates with diverse approaches +ceo_candidates = [ + CandidateProfile( + candidate_id="ceo_001", + name="Alexandra Martinez - Innovation CEO", + candidate_type=CandidateType.INDIVIDUAL, + policy_positions={ + "innovation_leadership": "Aggressive digital transformation", + "growth_strategy": "Rapid expansion with high-risk investments" + }, + leadership_style="transformational", + coordination_approach={"innovation": 0.9, "risk_taking": 0.8} + ), + # ... more CEO candidates +] + +# Create board CEO election +ceo_election = ElectionSwarm( + name="Board of Directors CEO Election", + voters=board_members, + candidates=ceo_candidates, + verbose=True +) + +# Run the election +result = ceo_election.run( + task="Elect the best CEO to lead our company", + election_type=ElectionAlgorithm.CONSENSUS +) + +# Get comprehensive statistics +stats = ceo_election.get_election_statistics() +print(f"Election statistics: {stats}") + +# Get swarm status +status = ceo_election.get_swarm_status() +print(f"Swarm status: {status}") +``` + +## Advanced Configuration + +### Election Configuration + +The ElectionSwarm uses a comprehensive configuration system with Pydantic validation. + +```python +from swarms.structs.election_swarm import ElectionConfig, ElectionConfigModel + +# Create configuration with validation +config = ElectionConfig( + config_data={ + "election_type": "orchestrator_selection", + "max_candidates": 5, + "max_voters": 50, + "enable_consensus": True, + "enable_leader_election": True, + "enable_matching": True, + "enable_coloring": True, + "enable_vertex_cover": True, + "enable_caching": True, + "enable_voter_tool_calls": True, + "batch_size": 10, + "max_workers": 5, + "budget_limit": 100.0, + "default_model": "gpt-4o-mini", + "verbose_logging": True + } +) + +# Validate configuration +errors = config.validate_config() +if errors: + print(f"Configuration errors: {errors}") + +# Save configuration to file +config.save_config("election_config.yaml") + +election = ElectionSwarm( + name="Advanced Election", + election_config=config, + voters=voters, + candidates=candidates +) +``` + +### ElectionConfigModel + +The configuration model with validation constraints: + +```python +class ElectionConfigModel(BaseModel): + election_type: str = Field(default="orchestrator_selection") + max_candidates: int = Field(default=5, ge=1, le=20) + max_voters: int = Field(default=100, ge=1, le=1000) + enable_consensus: bool = Field(default=True) + enable_leader_election: bool = Field(default=True) + enable_matching: bool = Field(default=True) + enable_coloring: bool = Field(default=True) + enable_vertex_cover: bool = Field(default=True) + enable_caching: bool = Field(default=True) + enable_voter_tool_calls: bool = Field(default=True) + batch_size: int = Field(default=25, ge=1, le=100) + max_workers: int = Field(default=10, ge=1, le=50) + budget_limit: float = Field(default=200.0, ge=0.0) + default_model: str = Field(default="gpt-4o-mini") + verbose_logging: bool = Field(default=False) +``` + +### Cost Tracking and Budget Management + +```python +# Monitor election costs +election = ElectionSwarm( + budget_limit=50.0, + enable_caching=True +) + +# Run election with cost monitoring +result = election.run(task="Select orchestrator") + +# Get cost statistics +stats = election.get_election_statistics() +print(f"Total cost: ${stats['cost_statistics']['total_cost']:.2f}") +print(f"Cache hit rate: {stats['cost_statistics']['cache_hit_rate']:.1%}") +``` + +## Core Methods + +### Step Method + +Execute a single step of the ElectionSwarm. + +```python +# Execute a single election step +result = election.step( + task="Select the best orchestrator for our team", + election_type=ElectionAlgorithm.CONSENSUS, + max_rounds=5 +) +``` + +### Run Method + +Run the ElectionSwarm for the specified number of loops. + +```python +# Run multiple election loops +result = election.run( + task="Elect a leader for our development team", + election_type=ElectionAlgorithm.LEADER_ELECTION, + max_rounds=3 +) +``` + +### Conduct Election + +Conduct an election using the specified algorithm. + +```python +# Conduct election directly +result = election.conduct_election( + election_type=ElectionAlgorithm.CONSENSUS, + max_rounds=5 +) +``` + +### Run Election Session + +Run a complete election session with comprehensive analysis. + +```python +# Run election session with detailed analysis +session_result = election.run_election_session( + election_type=ElectionAlgorithm.CONSENSUS, + max_rounds=5 +) + +# Access results +election_result = session_result["election_result"] +analysis = session_result["analysis"] +cost_statistics = session_result["cost_statistics"] +``` + +## Swarm Management Methods + +### Adding and Removing Participants + +```python +# Add new voter +new_voter = VoterProfile( + voter_id="agent_007", + name="New Agent", + voter_type=VoterType.EXPERT +) +election.add_voter(new_voter) + +# Add new candidate +new_candidate = CandidateProfile( + candidate_id="orchestrator_004", + name="New Orchestrator", + candidate_type=CandidateType.INDIVIDUAL +) +election.add_candidate(new_candidate) + +# Remove participants +election.remove_voter("agent_007") +election.remove_candidate("orchestrator_004") +``` + +### Swarm Status and Statistics + +```python +# Get swarm information +status = election.get_swarm_status() +print(f"Total participants: {status['total_participants']}") +print(f"Voters: {status['voters']}") +print(f"Candidates: {status['candidates']}") +print(f"Budget remaining: ${status['budget_remaining']:.2f}") + +# Get comprehensive statistics +stats = election.get_election_statistics() +print(f"Swarm name: {stats['swarm_info']['name']}") +print(f"Cache size: {stats['cache_stats']['cache_size']}") + +# Get swarm size +swarm_size = election.get_swarm_size() +print(f"Total swarm size: {swarm_size}") + +# Get specific voter or candidate +voter = election.get_voter("voter_001") +candidate = election.get_candidate("candidate_001") +``` + +### Reset and Cleanup + +```python +# Reset election swarm to initial state +election.reset() + +# This clears: +# - Conversation history +# - Cost tracking +# - Message protocol +# - Cache +``` + +## Best Practices + +### 1. Voter Design + +- **Diverse Expertise**: Include voters with different specializations +- **Balanced Preferences**: Ensure varied preference profiles +- **Neighbor Connections**: Create meaningful neighbor relationships +- **Realistic Demographics**: Use realistic agent characteristics + +### 2. Candidate Design + +- **Clear Platforms**: Define distinct policy positions +- **Compelling Promises**: Create realistic campaign promises +- **Relevant Experience**: Include pertinent background experience +- **Leadership Styles**: Vary leadership and coordination approaches + +### 3. Election Configuration + +- **Appropriate Algorithms**: Choose algorithms matching your use case +- **Budget Management**: Set realistic budget limits +- **Caching Strategy**: Enable caching for repeated elections +- **Logging Level**: Use verbose logging for debugging + +### 4. Performance Optimization + +- **Lazy Loading**: Enable lazy loading for large swarms +- **Batch Processing**: Use appropriate batch sizes +- **Parallel Execution**: Leverage concurrent operations +- **Cost Monitoring**: Track and optimize token usage + +## Error Handling + +The ElectionSwarm includes comprehensive error handling: + +```python +try: + result = election.run( + task="Select orchestrator", + election_type=ElectionAlgorithm.CONSENSUS + ) +except ValueError as e: + print(f"Configuration error: {e}") +except Exception as e: + print(f"Election failed: {e}") +``` + +Common error scenarios: + +- **No voters or candidates**: Ensure at least one participant +- **Invalid algorithm**: Use supported ElectionAlgorithm values +- **Budget exceeded**: Monitor and adjust budget limits +- **Agent loading failures**: Check agent configurations + +## Integration with Other Swarms + +The ElectionSwarm can be integrated with other Swarms architectures: + +```python +# Use with BoardOfDirectorsSwarm +from swarms.structs.board_of_directors_swarm import BoardOfDirectorsSwarm + +# Create board election +board_election = ElectionSwarm( + name="Board CEO Election", + voters=board_members, + candidates=ceo_candidates +) + +# Elect CEO +ceo_result = board_election.run( + task="Elect CEO for board governance", + election_type=ElectionAlgorithm.CONSENSUS +) + +# Use elected CEO with BoardOfDirectorsSwarm +board = BoardOfDirectorsSwarm( + chairman=ceo_result.winner, + # ... other board configuration +) +``` + +## Utility Functions + +### Create Default Configuration + +Create a default election configuration file. + +```python +from swarms.structs.election_swarm import create_default_election_config + +# Create default configuration file +create_default_election_config("my_election_config.yaml") +``` + +### Default Constants + +The ElectionSwarm uses several default constants: + +```python +DEFAULT_BUDGET_LIMIT = 200.0 +DEFAULT_CONSENSUS_THRESHOLD = 0.6 +DEFAULT_MAX_WORKERS = 10 +DEFAULT_MAX_ROUNDS = 5 +DEFAULT_BATCH_SIZE = 25 +``` + +## Conclusion + +The ElectionSwarm provides a robust, scalable solution for orchestrator selection in multi-agent systems. By implementing AGENTSNET communication protocols and multiple election algorithms, it enables sophisticated democratic decision-making processes that can adapt to various coordination requirements and communication complexities. + +Key advantages: + +- **Distributed Coordination**: Implements proven distributed computing algorithms +- **Flexible Architecture**: Supports various election types and configurations +- **Production Ready**: Includes cost tracking, caching, and comprehensive error handling +- **Swarms Integration**: Seamlessly integrates with other Swarms architectures +- **Real-world Applications**: Suitable for corporate governance, team leadership, and workflow orchestration + +The ElectionSwarm represents a significant advancement in multi-agent coordination, providing the tools necessary for sophisticated democratic decision-making in complex agent-based systems.